Part-of-Speech Tagging with Both Character and Word Information

DOI

10.2991/icence-16.2016.176How to use a DOI?

Keywords

Part-of-speech tagging; Character information; Word information; Maximum entropy.

Abstract

Part-of-speech tagging is to determine an appropriate grammatical category for each word in a sentence, which is one of the basic tasks of natural language processing. The former part-of-speech tagging methods mostly study the co-occurrence probability of the adjacent parts of speech at the word level, and lack the analysis of the internal structure of the word. In this paper, we propose a maximum entropy based Chinese part-of-speech tagger which not only uses word and part-of-speech information, but also uses character information inside the word. Our approach gives an error reduction of 61.3%, compared to the approach using only the word information.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - You Zhou
AU  - Fangzhou Liu
PY  - 2016/09
DA  - 2016/09
TI  - Part-of-Speech Tagging with Both Character and Word Information
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 945
EP  - 948
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.176
DO  - 10.2991/icence-16.2016.176
ID  - Zhou2016/09
ER  -